Which of the following types of analysis reveals association but not necessarily causation between data attributes?

• A. Correlation analysis
• B. Experimental analysis
• C. Causal inference analysis
• D. Time series forecasting

Answer: (A)

Verified step by step guidance

1. Understand the difference between correlation and causation: Correlation analysis measures the strength and direction of a relationship between two variables but does not imply that one variable causes the other.
2. Review the types of analysis given: Experimental analysis is designed to test causation by manipulating variables; causal inference analysis aims to establish cause-effect relationships; time series forecasting predicts future values based on past data patterns.
3. Recognize that correlation analysis only identifies whether variables move together (association), without proving that changes in one variable cause changes in another.
4. Recall that establishing causation requires controlled experiments or specific causal inference methods, which go beyond simple correlation.
5. Conclude that among the options, correlation analysis is the type that reveals association but not necessarily causation.